C
Casper Worm Hansen
Researcher at University of Copenhagen
Publications - 82
Citations - 1213
Casper Worm Hansen is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Life expectancy & Population. The author has an hindex of 16, co-authored 81 publications receiving 835 citations. Previous affiliations of Casper Worm Hansen include Aarhus University & University of Southern Denmark.
Papers
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Journal ArticleDOI
Modern gender roles and agricultural history: the Neolithic inheritance
TL;DR: This paper found that societies with long histories of agriculture have less equality in gender roles as a consequence of more patriarchal values and beliefs regarding the proper role of women in society than those without such long histories.
Journal ArticleDOI
Modern Gender Roles and Agricultural History: The Neolithic Inheritance
TL;DR: The authors found that societies with long histories of agriculture have less equality in gender roles as a consequence of more patriarchal values and beliefs regarding the proper role of women in society, and they tested this hypothesis in a world sample of countries, in regions of Europe, and among immigrants and children of immigrants living in the US.
Posted Content
Two Blades of Grass: The Impact of the Green Revolution
TL;DR: In this article, the authors examined the impact of the Green Revolution on aggregate economic outcomes in developing countries during the second half of the 20th century by using time variation in the development and diffusion of high-yielding crop varieties (HYVs), and the spatial variation in agro-climatically suitability for growing them, to identify the causal effects of adoption.
Proceedings ArticleDOI
Contextual and Sequential User Embeddings for Large-Scale Music Recommendation
Casper Worm Hansen,Christian Hansen,Lucas Maystre,Rishabh Mehrotra,Brian Brost,Federico Tomasi,Mounia Lalmas +6 more
TL;DR: This paper proposes CoSeRNN, a neural network architecture that models users’ preferences as a sequence of embeddings, one for each session, and finds that it outperforms the current state of the art by upwards of 10% on different ranking metrics.
Journal ArticleDOI
Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.
Espen Jimenez-Solem,Espen Jimenez-Solem,Tonny Studsgaard Petersen,Tonny Studsgaard Petersen,Casper Worm Hansen,Christian Hansen,Christina Lioma,Christian Igel,Wouter Boomsma,Oswin Krause,Stephan Sloth Lorenzen,Raghavendra Selvan,Janne Petersen,Janne Petersen,Martin Erik Nyeland,Mikkel Zöllner Ankarfeldt,Gert Mehl Virenfeldt,Matilde Winther-Jensen,Allan Linneberg,Mostafa Mehdipour Ghazi,Nicki Skafte Detlefsen,Andreas David Lauritzen,Abraham George Smith,Marleen de Bruijne,Marleen de Bruijne,Bulat Ibragimov,Jens E.V. Petersen,Martin Lillholm,Jon Middleton,Stine Hasling Mogensen,Hans-Christian Thorsen-Meyer,Anders Perner,Marie Helleberg,Benjamin Skov Kaas-Hansen,Mikkel Bonde,Alexander Bonde,Akshay Pai,Mads Nielsen,Martin Sillesen,Martin Sillesen +39 more
TL;DR: In this paper, a machine learning model was used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death from a cohort of approx 26 million citizens in Denmark.